Learn to analyze, visualize, and interpret biomechanics data using Python, AI, and real human movement examples.
This hands-on course bridges biomechanics and coding, built on the concepts from A Hands-On Guide to Biomechanics Data Analysis with Python and AI. You’ll learn how to process, analyze, and visualize human movement data using Python, Google Colab, and AI tools—no prior programming required. Step by step, we move from raw motion capture, force, and EMG signals to clear insights about posture, performance, and efficiency.
What you’ll learn
- Set up and work in Google Colab to run Python notebooks for biomechanics analysis, with zero installs..
- Load and inspect C3D motion capture files, including markers, analog signals like force plates and EMG, and key metadata..
- Read and write C3D in Python using ezc3d or c3dposeiq, then organize data for analysis..
- Build a tidy analysis table from C3D data by extracting time, marker trajectories, vertical ground reaction force, normalizing units, and filtering noise..
- Visualize biomechanics signals with matplotlib to create clear, publication-ready plots..
- Apply a practical workflow from input to export that you can reuse in labs or research..
Course Content
- Introduction –> 7 lectures • 36min.
- Input –> 5 lectures • 30min.
- Parsing –> 6 lectures • 32min.
- Analyze –> 7 lectures • 45min.
- Visualize –> 3 lectures • 28min.
- Export-Report –> 3 lectures • 21min.
- From MoCap Data to NeuroMSK Models to Machine Learning –> 4 lectures • 35min.
Requirements
This hands-on course bridges biomechanics and coding, built on the concepts from A Hands-On Guide to Biomechanics Data Analysis with Python and AI. You’ll learn how to process, analyze, and visualize human movement data using Python, Google Colab, and AI tools—no prior programming required. Step by step, we move from raw motion capture, force, and EMG signals to clear insights about posture, performance, and efficiency.
Through guided notebooks and real datasets, you’ll explore:
- Data cleaning, filtering, and event detection in biomechanics
- Force-plate and motion data analysis with NumPy and Pandas
- 2D/3D visualization and report generation in Colab
- Basic machine learning for movement classification and prediction
You’ll also gain practical skills for parsing C3D files, aligning markers and forces, normalizing units, detecting gait events, and computing key metrics such as stride time, GRF peaks, and symmetry indices. Each module follows the same reproducible pipeline used by biomechanics labs worldwide—
Input → Parse → Analyze → Visualize → Report.
By the end, you’ll be able to transform complex biomechanical data into meaningful, shareable results—ready for research, clinical work, sports analysis, or AI modeling. Includes Colab notebooks, sample datasets, code templates, and report builders so you can apply everything immediately to your own projects.
Who is it for? Students, clinicians, coaches, and researchers seeking a practical, modern toolkit. You’ll complete bite-size projects (e.g., compare shoes or techniques) and a capstone that imports C3D/CSV, computes key features, visualizes cycles, and exports an HTML/CSV mini-report. Clear checklists, guardrails, and starter code keep you moving—from first plot to publishable, reproducible results.